Search

This paper addresses the problem of single image de-raining, that is, the task of recovering clean and rain-free background scenes from a single image obscured by a rainy artifact. Although recent advances adopt real-world time-lapse data to overcome the need for paired rain-clean images, they are limited to fully exploit the time-lapse data. The main cause is that, in terms of network architectures, they could not capture long-term rain streak information in the time-lapse data during training owing to the lack of memory components. To address this problem, we propose a novel network architecture combining the time-lapse data and, the memory network that explicitly helps to capture long-term rain streak information. Our network comprises the encoder-decoder networks and a memory network. The features extracted from the encoder are read and updated in the memory network that contains several memory items to store rain streak-aware feature representations. With the read/update operation, the memory network retrieves relevant memory items in terms of the queries, enabling the memory items to represent the various rain streaks included in the time-lapse data. To boost the discriminative power of memory features, we also present a novel background selective whitening (BSW) loss for capturing only rain streak information in the memory network by erasing the background information. Experimental results on standard benchmarks demonstrate the effectiveness and superiority of our approach. [ABSTRACT FROM AUTHOR]

Despite significant progress in machine learning, pedestrian detection in the real-world is still regarded as one of the challenging problems, limited by occluded appearances, cluttered backgrounds, and bad visibility at night. This has caused detection approaches using multi-spectral sensors such as color and thermal which could be complementary to each other. In this paper, we propose a novel sensor fusion framework for detecting pedestrians even in challenging real-world environments. We design a convolutional neural network (CNN) architecture that consists of three-branch detection models taking different modalities as inputs. Unlike existing methods, we consider all detection probabilities from each modality in a unified CNN framework and selectively use them through a channel weighting fusion (CWF) layer to maximize the detection performance. An accumulated probability fusion (APF) layer is also introduced to combine probabilities from different modalities at the proposal-level. We formulate these sub-networks into a unified network, so that it is possible to train the whole network in an end-to-end manner. Our extensive evaluation demonstrates that the proposed method outperforms the state-of-the-art methods on the challenging KAIST, CVC-14, and DIML multi-spectral pedestrian datasets. [ABSTRACT FROM AUTHOR]

Interactive image segmentation has remained an active research topic in image processing and graphics, since the user intention can be incorporated to enhance the performance. It can be employed to mobile devices which now allow user interaction as an input, enabling various applications. Most interactive segmentation methods assume that the initial labels are correctly and carefully assigned to some parts of regions to segment. Inaccurate labels, such as foreground labels in background regions for example, lead to incorrect segments, even by a small number of inaccurate labels, which is not appropriate for practical usage such as mobile application. In this paper, we present an interactive segmentation method that is robust to inaccurate initial labels (scribbles). To address this problem, we propose a structure-aware labeling method using occurrence and co-occurrence probability (OCP) of color values for each initial label in a unified framework. Occurrence probability captures a global distribution of all color values within each label, while co-occurrence one encodes a local distribution of color values around the label. We show that nonlocal regularization together with the OCP enables robust image segmentation to inaccurately assigned labels and alleviates a small-cut problem. We analyze theoretic relations of our approach to other segmentation methods. Intensive experiments with synthetic and manual labels show that our approach outperforms the state of the art. [ABSTRACT FROM AUTHOR]

One aim of detection proposal methods is to reduce the computational overhead of object detection. However, most of the existing methods have significant computational overhead for real-time detection on mobile devices. A fast and accurate proposal method of human detection called personness estimation is proposed, which facilitates real-time human detection on mobile devices and can be effectively integrated into part-based detection, achieving high detection performance at a low computational cost. Our work is based on two observations: (i) normed gradients, which are designed for generic objectness estimation, effectively generate high-quality detection proposals for the person category; (ii) fusing the normed gradients with color attributes improves the performance of proposal generation for human detection. Thus, the candidate windows generated by the personness estimation will very likely contain human subjects. The human detection is then guided by the candidate windows, offering high detection performance even when the detection task terminates prior to completion. This interruptible detection scheme, called anytime detection, enables real-time human detection on mobile devices. Furthermore, we introduce a new evaluation methodology called time-recall curves to practically evaluate our approach. The applicability of our proposed method is demonstrated in extensive experiments on a publicly available dataset and a real mobile device, facilitating acquisition and enhancement of portrait photographs (e.g. selfie) on widespread mobile platforms. [ABSTRACT FROM AUTHOR]

Establishing correspondences is a fundamental task in many image processing and computer vision applications. In particular, finding the correspondences between a non-linearly deformed image pair induced by different modality conditions is a challenging problem. This paper describes a simple but powerful image transform called local area transform (LAT) for modality-robust correspondence estimation. Specifically, LAT transforms an image from the intensity domain to the local area domain, which is invariant under nonlinear intensity deformations, especially radiometric, photometric, and spectral deformations. Experimental results show that LATransformed images provide a consistency for nonlinearly deformed images, even under random intensity deformations. LAT reduces the mean absolute difference by approximately 0.20 and the different pixel ratio by approximately 58% on average, as compared to conventional methods. Furthermore, the reformulation of descriptors with LAT shows superiority to conventional methods, which is a promising result for the tasks of cross-spectral and modality correspondence matching. LAT gains an approximately 23% improvement in the correct detection ratio and a 10% improvement in the recognition rate for the tasks of RGB-NIR cross-spectral template matching and cross-spectral feature matching, respectively. LAT reduces the bad pixel percentage by approximately 15% and the root mean squared errors by 13.5 in the task of cross-radiation stereo matching. LAT also improves the cross-modal dense flow estimation task in terms of warping error, providing 50% error reduction. [ABSTRACT FROM AUTHOR]

Foreground detection is one of the most important and fundamental tasks in many computer vision applications such as real-time video surveillance. Although there have been many efforts to find solutions to this problem, many obstacles such as illumination changes, noises, dynamic backgrounds, and computational complexities have prevented them from being used in real surveillance systems. In this paper, to alleviate these inherent limitations of conventional methods, we propose a fast illumination-robust foreground detection (FIFD) system that provides robustness against illumination variations and noises from various real circumstances with an efficient computational scheme. In contrast to the conventional approaches, our method focuses on efficiently formulating the foreground object detection system by leveraging a foreground candidate region detection and hierarchical distribution map. Specifically, our approach consists of three parts. First, for a query image, foreground candidates are detected by fusing multiple methods. The existence and the block size of the foreground object are determined through the use of the foreground continuity. Second, the foreground block is found from the estimated distribution map and then detected from the extracted valid blocks. Finally, with a labeling scheme, the foreground is detected. To intensively evaluate our approach compared to the conventional methods, we use the publicly available I2R and traffic datasets, and we build a novel electron multiplying charge-coupled device foreground detection benchmark taken in an environment with light lower than 10lux. Experimental results show that our approach provides satisfactory performance compared to the state-of-the-art methods even under very challenging circumstances. Furthermore, our approach is very efficient in that it takes only approximately 31 ms per frame. [ABSTRACT FROM AUTHOR]

This paper presents a deep architecture, called pyramidal semantic correspondence networks (PSCNet), that estimates locally-varying affine transformation fields across semantically similar images. To deal with large appearance and shape variations that commonly exist among different instances within the same object category, we leverage a pyramidal model where the affine transformation fields are progressively estimated in a coarse-to-fine manner so that the smoothness constraint is naturally imposed. Different from the previous methods which directly estimate global or local deformations, our method first starts to estimate the transformation from an entire image and then progressively increases the degree of freedom of the transformation by dividing coarse cell into finer ones. To this end, we propose two spatial pyramid models by dividing an image in a form of quad-tree rectangles or into multiple semantic elements of an object. Additionally, to overcome the limitation of insufficient training data, a novel weakly-supervised training scheme is introduced that generates progressively evolving supervisions through the spatial pyramid models by leveraging a correspondence consistency across image pairs. Extensive experimental results on various benchmarks including TSS, Proposal Flow-WILLOW, Proposal Flow-PASCAL, Caltech-101, and SPair-71k demonstrate that the proposed method outperforms the lastest methods for dense semantic correspondence. [ABSTRACT FROM AUTHOR]

We propose a novel framework for spatiotemporal action detection using only video-level class labels as weak supervision. Traditional fully-supervised approaches rely on labor-intensive manual annotation of bounding boxes for each frame. In contrast, collecting video-level class labels is significantly less tedious and more feasible compared to annotating frame-level sequences with bounding boxes. To address this challenge, we propose a discriminative action tubelet detector, called DAT-detector, designed to discern discriminative tubelets from action tubelet proposals (ATPs). Whereas the previous approaches have only focused on tubelet selection among the predefined object proposals, our DAT-detector prioritizes the generation of more precise action tubelets using regression and attention modules. Moreover, we introduce an ATP generation method that enhances the quality of tubelet proposals. Our approach achieves state-of-the-art performance on several benchmarks, and also demonstrates competitive performance even with fully-supervised approaches. • We propose a DAT-detector for spatiotemporal action detection using video-level class labels as weak supervision. • The DAT-detector generates precise actions tubes via proposed attention and regression modules. • We enhace tubelet proposal quality with our action tubelet proposal generation method. • Our method significantly outperforms the state-of-the-art action proposal methods. • We achieve remarkable performance in spatiotemporal action detection across multiple benchmarks, effectively competing with fully supervised approaches. [ABSTRACT FROM AUTHOR]

Unveiling the dense correspondence under the haze layer remains a challenging task, since the scattering effects result in less distinctive image features. Contrarily, dehazing is often confused by the airlight-albedo ambiguity which cannot be resolved independently at each pixel. In this paper, we introduce a deep convolutional neural network that simultaneously estimates a disparity and clear image from a hazy stereo image pair. Both tasks are synergistically formulated by fusing depth information from the matching cost and haze transmission. To learn the optimal fusion of depth-related features, we present a novel encoder-decoder architecture that extends the core idea of attention mechanism to the simultaneous stereo matching and dehazing. As a result, our method estimates high-quality disparity for the stereo images in scattering media, and produces appearance images with enhanced visibility. Finally, we further propose an effective strategy for adaptation to camera-captured images by distilling the cross-domain knowledge. Experiments on both synthetic and real-world scenarios including comparisons with state-of-the-art methods demonstrate the effectiveness and flexibility of our approach. [ABSTRACT FROM AUTHOR]

Typically, hierarchical reinforcement learning (RL) requires skills that are applicable to various downstream tasks. Although several recent studies have proposed the supervised and unsupervised learning of such skills, the learned skills are often entangled, which hinders their interpretation. To alleviate this, we propose a novel method to use weak labels for learning disentangled skills from the continuous latent representations of trajectories. To this end, we extended a trajectory variational autoencoder (VAE) to impose an inductive bias using weak labels, which explicitly enforces the disentangling of the trajectory representations into factors of interest intended for the model to learn. Using the latent representations as skills, a skill-based policy network is trained to generate trajectories similar to the learned decoder of the trajectory VAE. Furthermore, using the disentangled skill, we propose a skill repetition that can expand the entire trajectories generated by the policy at test time, resulting in an effective planning strategy. Experiments were performed on several challenging navigation tasks in mazes, and the results demonstrate the effectiveness of our method at solving hierarchical RL problems even with a long horizon and sparse rewards. [Display omitted] • Learning skills as continuous latent variables with disentangled factors. • Using weak labels to train a trajectory variational autoencoder. • Skill repetition to expand the whole trajectory. [ABSTRACT FROM AUTHOR]

Highlights • A self-supervised learning which does not require human annotations for training CNN. • Learning the correct arrangement of object proposals to represent an image by CNN. • Demonstrating the advantage of our model by applying it to PASCAL VOC datasets. • Application to other vision tasks including image retrieval and semantic matching. Abstract Learning visual representations plays an important role in computer vision and machine learning applications. It facilitates a model to understand and perform high-level tasks intelligently. A common approach for learning visual representations is supervised one which requires a huge amount of human annotations to train the model. This paper presents a self-supervised approach which learns visual representations from input images without human annotations. We learn the correct arrangement of object proposals to represent an image using a convolutional neural network (CNN) without any manual annotations. We hypothesize that the network trained for solving this problem requires the embedding of semantic visual representations. Unlike existing approaches that use uniformly sampled patches, we relate object proposals that contain prominent objects and object parts. More specifically, we discover the representation that considers overlap, inclusion, and exclusion relationship of proposals as well as their relative position. This allows focusing on potential objects and parts rather than on clutter. We demonstrate that our model outperforms existing self-supervised learning methods and can be used as a generic feature extractor by applying it to object detection, classification, action recognition, image retrieval, and semantic matching tasks. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel framework for monocular depth estimation via a student–teacher strategy. • Introducing a data ensemble and stereo confidence-guided regression loss. • Constructing a new large-scale outdoor stereo dataset named the DIML/CVL dataset. • Demonstrating the feature representation of our trained-model for high-level tasks. Current self-supervised methods for monocular depth estimation are largely based on deeply nested convolutional networks that leverage stereo image pairs or monocular sequences during the training phase. However, they often exhibit inaccurate results around occluded regions and depth boundaries. In this paper, we present a simple yet effective approach for monocular depth estimation using stereo image pairs. The study aims to propose a student–teacher strategy in which a shallow student network is trained with the auxiliary information obtained from a deeper and more accurate teacher network. Specifically, we first train the stereo teacher network by fully utilizing the binocular perception of 3-D geometry, and then use the depth predictions of the teacher network to train the student network for monocular depth inference. This enables us to exploit all available depth data from massive unlabeled stereo pairs. We propose a strategy that involves the use of a data ensemble to merge the multiple depth predictions of the teacher network to improve the training samples by collecting non-trivial knowledge beyond a single prediction. To refine the inaccurate depth estimation that is used when training the student network, we further propose stereo confidence guided regression loss that handles the unreliable pseudo depth values in occlusion, texture-less region, and repetitive pattern. To complement the existing dataset comprising outdoor driving scenes, we built a novel large-scale dataset consisting of one million outdoor stereo images taken using hand-held stereo cameras. Finally, we demonstrate that the monocular depth estimation network provides feature representations that are suitable for high-level vision tasks. The experimental results for various outdoor scenarios demonstrate the effectiveness and flexibility of our approach, which outperforms state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Person search aims to localize a person of interest in a large image gallery captured by multiple, non-overlapping cameras. Prevalent unified methods have suffered from (1) noisy proposals with mis-detection and occlusion, and (2) large appearance variation within a class, which deteriorates the prototype-based metric learning. To address these problems, we introduce a Prototype-guided Attention Distillation, shortly PAD, which exploits a prototype (a typical representation of an identity) as a guidance to the attention module to consistently highlight identity-inherent regions across different poses. To utilize the knowledge encoded in prototypes for matching unseen IDs, PAD conducts attention distillation to guide student Re-ID queries by deeply mimicking attention maps from the prototype query. Additionally, to address large intra-class variation induced by pose or camera views, we extend PAD with multiple part prototypes representing consistent local regions across different instances. Furthermore, we exploit an adaptive momentum strategy for robust attention distillation in PAD to update more distinct prototypes. Extensive experiments conducted on CUHK-SYSU and PRW demonstrate the effectiveness of PAD, showcasing state-of-the-art performance. Moreover, our distilled attention surprisingly highlights distinguished multiple regions for person search.

Most variational formulations for structure-texture image decomposition force structure images to have small norm in some functional spaces, and share a common notion of edges, i.e., large-gradients or -intensity differences. However, such definition makes it difficult to distinguish structure edges from oscillations that have fine spatial scale but high contrast. In this paper, we introduce a new model by learning deep variational prior for structure images without explicit training data. An alternating direction method of multiplier (ADMM) algorithm and its modular structure are adopted to plug deep variational priors into an iterative smoothing process. The central observations are that convolution neural networks (CNNs) can replace the total variation prior, and are indeed powerful to capture the natures of structure and texture. We show that our learned priors using CNNs successfully differentiate highamplitude details from structure edges, and avoid halo artifacts. Different from previous data-driven smoothing schemes, our formulation provides another degree of freedom to produce continuous smoothing effects. Experimental results demonstrate the effectiveness of our approach on various computational photography and image processing applications, including texture removal, detail manipulation, HDR tone-mapping, and nonphotorealistic abstraction.

This paper presents a texture flow estimation method that uses an appearance-space clustering and a correspondence search in the space of deformed exemplars. To estimate the underlying texture flow, such as scale, orientation, and texture label, most existing approaches require a certain amount of user interactions. Strict assumptions on a geometric model further limit the flow estimation to such a near-regular texture as a gradient-like pattern. We address these problems by extracting distinct texture exemplars in an unsupervised way and using an efficient search strategy on a deformation parameter space. This enables estimating a coherent flow in a fully automatic manner, even when an input image contains multiple textures of different categories. A set of texture exemplars that describes the input texture image is first extracted via a medoid-based clustering in appearance space. The texture exemplars are then matched with the input image to infer deformation parameters. In particular, we define a distance function for measuring a similarity between the texture exemplar and a deformed target patch centered at each pixel from the input image, and then propose to use a randomized search strategy to estimate these parameters efficiently. The deformation flow field is further refined by adaptively smoothing the flow field under guidance of a matching confidence score. We show that a local visual similarity, directly measured from appearance space, explains local behaviors of the flow very well, and the flow field can be estimated very efficiently when the matching criterion meets the randomized search strategy. Experimental results on synthetic and natural images show that the proposed method outperforms existing methods.

This paper presents a depth superresolution (SR) method that uses both of a low-resolution (LR) depth image and a high-resolution (HR) intensity image. We formulate depth SR as a graph-based transduction problem. In particular, the HR intensity image is represented as an undirected graph, in which pixels are characterized as vertices, and their relations are encoded as an affinity function. When the vertices initially labeled with certain depth hypotheses (from the LR depth image) are regarded as input queries, all the vertices are scored with respect to the relevances to these queries by a classifying function. Each vertex is then labeled with the depth hypothesis that receives the highest relevance score. We design the classifying function by considering the local and global structures of the HR intensity image. This approach enables us to address a depth bleeding problem that typically appears in current depth SR methods. Furthermore, input queries are assigned in a probabilistic manner, making depth SR robust to noisy depth measurements. We also analyze existing depth SR methods in the context of transduction, and discuss their theoretic relations. Intensive experiments demonstrate the superiority of the proposed method over state-of-the-art methods both qualitatively and quantitatively.

This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memory-and computation-intensive large linear system, defined over a d-dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs ∼10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform Lγ norm smoothing (0 < γ < 2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications.

In this paper, a probability-based rendering (PBR) method is described for reconstructing an intermediate view with a steady-state matching probability (SSMP) density function. Conventionally, given multiple reference images, the intermediate view is synthesized via the depth image-based rendering technique in which geometric information (e.g., depth) is explicitly leveraged, thus leading to serious rendering artifacts on the synthesized view even with small depth errors. We address this problem by formulating the rendering process as an image fusion in which the textures of all probable matching points are adaptively blended with the SSMP representing the likelihood that points among the input reference images are matched. The PBR hence becomes more robust against depth estimation errors than existing view synthesis approaches. The MP in the steady-state, SSMP, is inferred for each pixel via the random walk with restart (RWR). The RWR always guarantees visually consistent MP, as opposed to conventional optimization schemes (e.g., diffusion or filtering-based approaches), the accuracy of which heavily depends on parameters used. Experimental results demonstrate the superiority of the PBR over the existing view synthesis approaches both qualitatively and quantitatively. Especially, the PBR is effective in suppressing flicker artifacts of virtual video rendering although no temporal aspect is considered. Moreover, it is shown that the depth map itself calculated from our RWR-based method (by simply choosing the most probable matching point) is also comparable with that of the state-of-the-art local stereo matching methods.

In this paper, the origin of random walk with restart (RWR) and its generalization are described. It is well known that the random walk (RW) and the anisotropic diffusion models share the same energy functional, i.e., the former provides a steady-state solution and the latter gives a flow solution. In contrast, the theoretical background of the RWR scheme is different from that of the diffusion-reaction equation, although the restarting term of the RWR plays a role similar to the reaction term of the diffusion-reaction equation. The behaviors of the two approaches with respect to outliers reveal that they possess different attributes in terms of data propagation. This observation leads to the derivation of a new energy functional, where both volumetric heat capacity and thermal conductivity are considered together, and provides a common framework that unifies both the RW and the RWR approaches, in addition to other regularization methods. The proposed framework allows the RWR to be generalized (GRWR) in semilocal and nonlocal forms. The experimental results demonstrate the superiority of GRWR over existing regularization approaches in terms of depth map up-sampling and interactive image segmentation.

In this paper, a space-time hole filling approach is presented to deal with a disocclusion when a view is synthesized for the 3D video. The problem becomes even more complicated when the view is extrapolated from a single view, since the hole is large and has no stereo depth cues. Although many techniques have been developed to address this problem, most of them focus only on view interpolation. We propose a space-time joint filling method for color and depth videos in view extrapolation. For proper texture and depth to be sampled in the following hole filling process, the background of a scene is automatically segmented by the random walker segmentation in conjunction with the hole formation process. Then, the patch candidate selection process is formulated as a labeling problem, which can be solved with random walks. The patch candidates that best describe the hole region are dynamically selected in the space-time domain, and the hole is filled with the optimal patch for ensuring both spatial and temporal coherence. The experimental results show that the proposed method is superior to state-of-the-art methods and provides both spatially and temporally consistent results with significantly reduced flicker artifacts.

Anisotropic diffusion has been known to be closely related to adaptive smoothing and discretized in a similar manner. This paper revisits a fundamental relationship between two approaches. It is shown that adaptive smoothing and anisotropic diffusion have different theoretical backgrounds by exploring their characteristics with the perspective of normalization, evolution step size, and energy flow. Based on this principle, adaptive smoothing is derived from a second order partial differential equation (PDE), not a conventional anisotropic diffusion, via the coupling of Fick's law with a generalized continuity equation where a "source" or "sink" exists, which has not been extensively exploited. We show that the source or sink is closely related to the asymmetry of energy flow as well as the normalization term of adaptive smoothing. It enables us to analyze behaviors of adaptive smoothing, such as the maximum principle and stability with a perspective of a PDE. Ultimately, this relationship provides new insights into application-specific filtering algorithm design. By modeling the source or sink in the PDE, we introduce two specific diffusion filters, the robust anisotropic diffusion and the robust coherence enhancing diffusion, as novel instantiations which are more robust against the outliers than the conventional filters.

This paper describes a robust scale-space filter that adaptively changes the amount of flux according to the local topology of the neighborhood. In a manner similar to modeling heat or temperature flow in physics, the robust scale-space filter is derived by coupling Fick's law with a generalized continuity equation in which the source or sink is modeled via a specific heat capacity. The filter plays an essential part in two aspects. First, an evolution step size is adaptively scaled according to the local structure, enabling the proposed filter to be numerically stable. Second, the influence of outliers is reduced by adaptively compensating for the incoming flux. We show that classical diffusion methods represent special cases of the proposed filter. By analyzing the stability condition of the proposed filter, we also verify that its evolution step size in an explicit scheme is larger than that of the diffusion methods. The proposed filter also satisfies the maximum principle in the same manner as the diffusion. Our experimental results show that the proposed filter is less sensitive to the evolution step size, as well as more robust to various outliers, such as Gaussian noise, impulsive noise, or a combination of the two.

This paper presents a novel method for cost aggregation and occlusion handling for stereo matching. In order to estimate optimal cost, given a per-pixel difference image as observed data, we define an energy function and solve the minimization problem by solving the iterative equation with the numerical method. We improve performance and increase the convergence rate by using several acceleration techniques such as the Gauss-Seidel method, the multiscale approach, and adaptive interpolation. The proposed method is computationally efficient since it does not use color segmentation or any global optimization techniques. For occlusion handling, which has not been performed effectively by any conventional cost aggregation approaches, we combine the occlusion problem with the proposed minimization scheme. Asymmetric information is used so that few additional computational loads are necessary. Experimental results show that performance is comparable to that of many state-of-the-art methods. The proposed method is in fact the most successful among all cost aggregation methods based on standard stereo test beds.

We describe a face recognition system based on two different three-dimensional (3D) sensors. We use 3D sensors to overcome the pose-variation problems that cannot be effectively solved in two-dimensional images. We acquire input data based on a structured-light system and compare it with 3D faces that are obtained from a 3D laser scanner. Owing to differences in structure between the input data and the 3D faces, we can generate the range images of the probe and stored images. For estimating the head pose of input data, we propose a novel error-compensated singular-value decomposition that geometrically estimates the rotation angle. Face recognition rates obtained with principal component analysis on various range images of 35 people in different poses show promising results.